{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文件下载成功！\n"
     ]
    }
   ],
   "source": [
    "url = \"https://gitee.com/shuxxbclass/2025summber/raw/master/python%E5%AE%9E%E8%B7%B5/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/data/pricing_data.csv\"\n",
    "response = requests.get(url)\n",
    "\n",
    "if response.status_code == 200:\n",
    "    with open(\"pricing_data.csv\", \"wb\") as f:\n",
    "        f.write(response.content)\n",
    "    print(\"文件下载成功！\")\n",
    "else:\n",
    "    print(f\"下载失败，状态码：{response.status_code}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据读取成功\n"
     ]
    }
   ],
   "source": [
    "file_path=\"pricing_data.csv\"\n",
    "try:\n",
    "    data=pd.read_csv(file_path)\n",
    "    print(\"数据读取成功\")\n",
    "except FileNotFoundError:\n",
    "    print(f\"错误：未找到文件 {file_path},请检查路径是否正确\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[[\"Price\"]]\n",
    "y=data[\"Sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "截距（b0）:1007.94\n",
      "系数（b1）:-8.15\n"
     ]
    }
   ],
   "source": [
    "print(f\"截距（b0）:{model.intercept_:.2f}\")\n",
    "print(f\"系数（b1）:{model.coef_[0]:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, color='blue',label='realdata')\n",
    "plt.plot(X.values, model.predict(X),color='red',linewidth=2,label='regressionmodel')\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('sales')\n",
    "plt.title('regression between price and sales')\n",
    "plt.legend\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
